2025-12-04
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This presentation summarizes:
Last Gift Cohort
Body Donation
Autopsy
Tissue Collection
HIV Sequencing
Phylogenetics and Modeling
Image attribution: BioRender.
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Note
Viral Suppression: Individuals with detectable viral load >1000 copies/µl are indicated with an *.
Viral Suppression: Individuals with detectable viral load >1000 copies/µl are indicated with an *.
Viral Suppression: Individuals with detectable viral load >1000 copies/µl are indicated with an *.
Viral Suppression: Individuals with detectable viral load >1000 copies/µl are indicated with an *.
Conceptual motivation
In a transition model, exposure represents the “opportunity” for observing a migration event between compartments. We considered the following definition:
\[ n_{\text{states}, i} : \text{number of Markov states in run } i \times \text{number of sequences from CNS} \times \text{number of sequences from Periphery} \]
data %>%
mutate(
pairs_cns = ntissues_cns * (ntissues_cns - 1) / 2,
pairs_periph = ntissues_periph * (ntissues_periph - 1) / 2,
exposure = case_when(
migration_type == "cross_BBB" & direction == "CNS to Periph" ~
n_states * nseq_cns * nseq_periph,
migration_type == "cross_BBB" & direction == "Periphery to CNS" ~
n_states * nseq_periph * nseq_cns,
migration_type == "within_CNS" ~
n_states * pairs_cns * (nseq_cns^2),
migration_type == "within_peripheral" ~
n_states * pairs_periph * (nseq_periph^2)
)
)
Notes
\[ \log(\mu_i) = \beta_0 + \beta_1 \cdot \text{Marker}_i + b_{\text{pid}(i)} + \log(\text{Exposure}_i) \]
fit_models <- function(marker, data_in) {
formula <- as.formula(
paste0(
"n_events ~ ", marker,
" + age + sex + last_cd4_t_cell_count + duration_infection_years +
(1 | pid) + offset(log_exposure)"
)
)
glmmTMB(
formula,
data = data_in,
family = nbinom2(),
control = glmmTMBControl(
optimizer = optim,
optArgs = list(method = "BFGS", maxit = 5000)
)
)
}
fit_models <- function(marker, data_in) {
formula <- as.formula(
paste0(
"n_events ~ ", marker,
" + age + sex + last_cd4_t_cell_count + duration_infection_years +
dna_level_from + dna_level_to +
(1 | pid) + offset(log_exposure)"
)
)
glmmTMB(
formula,
data = data_in,
family = nbinom2(),
control = glmmTMBControl(
optimizer = optim,
optArgs = list(method = "BFGS", maxit = 5000)
)
)
}
Viral Suppression: Individuals with detectable viral load >1000 copies/µl are excluded (eligible: LG44 and LG48).
| Associations between CSF biomarkers and counts of migration events from the CNS to the periphery | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Negative Binomial Models | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 13 | Ok | ✅ | 1.042 | 0.7996–1.36 | 0.759 | ⬆ | ||
| GM-CSF | 8 | Ok | ✅ | 0.890 | 0.7506–1.06 | 0.180 | ⬇ | ||
| GRO-alpha (CXCL1) | 13 | Ok | ✅ | 0.988 | 0.9805–1.00 | 0.001 | ** | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 0.236 | 0.0031–17.88 | 0.513 | ⬇ | ||
| IL-1β | 5 | Ok | ✅ | 0.839 | 0.6518–1.08 | 0.173 | ⬇ | ||
| IL-1RA | 12 | Ok | ✅ | 1.000 | 0.9995–1.00 | 0.000 | *** | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.419 | 0.2598–0.67 | 0.000 | *** | ⬇ | |
| IL-5 | 5 | Ok | ✅ | 0.985 | 0.8074–1.20 | 0.877 | ⬇ | ||
| IL-6 | 12 | Ok | ✅ | 0.998 | 0.9971–1.00 | 0.000 | *** | ⬇ | |
| IL-7 | 13 | Ok | ✅ | 0.245 | 0.0441–1.37 | 0.109 | ⬇ | ||
| IL-8 (CXCL8) | 13 | Ok | ✅ | 0.998 | 0.9954–1.00 | 0.089 | . | ⬇ | |
| IL-9 | 1 | Sparse Data | ❌ | 1.165 | 0.6152–2.20 | 0.640 | ⬆ | ||
| IL-10 | 8 | Ok | ✅ | 0.511 | 0.2750–0.95 | 0.033 | * | ⬇ | |
| IL-15 | 5 | Ok | ✅ | 0.491 | 0.1756–1.38 | 0.176 | ⬇ | ||
| IL-17A/CTLA-8 | 1 | Sparse Data | ❌ | 0.327 | 0.0442–2.42 | 0.274 | ⬇ | ||
| IL-18 | 13 | Ok | ✅ | 1.005 | 0.9827–1.03 | 0.686 | ⬆ | ||
| IL-31 | 1 | Sparse Data | ❌ | 0.926 | 0.6711–1.28 | 0.641 | ⬇ | ||
| IP-10 (CXCL10) | 13 | Ok | ✅ | 1.000 | 0.9969–1.00 | 0.926 | ⬇ | ||
| MCP-1 (CCL2) | 13 | Ok | ✅ | 1.000 | 0.9988–1.00 | 0.953 | ⬇ | ||
| MIP-1α (CCL3) | 13 | Ok | ✅ | 0.871 | 0.7684–0.99 | 0.031 | * | ⬇ | |
| MIP-1β (CCL4) | 13 | Ok | ✅ | 0.965 | 0.9424–0.99 | 0.002 | ** | ⬇ | |
| RANTES (CCL5) | 13 | Ok | ✅ | 1.009 | 0.9194–1.11 | 0.849 | ⬆ | ||
| SDF-1α | 13 | Ok | ✅ | 1.000 | 0.9994–1.00 | 0.751 | ⬆ | ||
| TNF-α | 2 | Sparse Data | ❌ | 0.326 | 0.2118–0.50 | 0.000 | *** | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
| Associations between CSF biomarkers and counts of migration events from the periphery to the CNS | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Negative Binomial Models | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 16 | Ok | ✅ | 0.970 | 0.864–1.09 | 0.607 | ⬇ | ||
| GM-CSF | 11 | Ok | ✅ | 0.878 | 0.780–0.99 | 0.031 | * | ⬇ | |
| GRO-alpha (CXCL1) | 16 | Ok | ✅ | 0.986 | 0.981–0.99 | 0.000 | *** | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 0.230 | 0.014–3.92 | 0.310 | ⬇ | ||
| IL-1β | 7 | Ok | ✅ | 0.873 | 0.753–1.01 | 0.072 | . | ⬇ | |
| IL-1RA | 15 | Ok | ✅ | 1.000 | 1.000–1.00 | 0.001 | *** | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.380 | 0.304–0.48 | 0.000 | *** | ⬇ | |
| IL-5 | 7 | Ok | ✅ | 0.915 | 0.713–1.17 | 0.486 | ⬇ | ||
| IL-6 | 15 | Ok | ✅ | 0.998 | 0.997–1.00 | 0.000 | *** | ⬇ | |
| IL-7 | 16 | Ok | ✅ | 0.866 | 0.232–3.23 | 0.831 | ⬇ | ||
| IL-8 (CXCL8) | 16 | Ok | ✅ | 0.999 | 0.997–1.00 | 0.162 | ⬇ | ||
| IL-9 | 1 | Sparse Data | ❌ | 1.205 | 0.712–2.04 | 0.487 | ⬆ | ||
| IL-10 | 10 | Ok | ✅ | 0.916 | 0.765–1.10 | 0.339 | ⬇ | ||
| IL-15 | 7 | Ok | ✅ | 0.378 | 0.183–0.78 | 0.009 | ** | ⬇ | |
| IL-17A/CTLA-8 | 2 | Sparse Data | ❌ | 0.584 | 0.225–1.51 | 0.268 | ⬇ | ||
| IL-18 | 16 | Ok | ✅ | 1.011 | 0.995–1.03 | 0.179 | ⬆ | ||
| IL-31 | 1 | Sparse Data | ❌ | 0.908 | 0.718–1.15 | 0.422 | ⬇ | ||
| IP-10 (CXCL10) | 16 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.398 | ⬇ | ||
| MCP-1 (CCL2) | 16 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.862 | ⬇ | ||
| MIP-1α (CCL3) | 16 | Ok | ✅ | 0.904 | 0.839–0.97 | 0.008 | ** | ⬇ | |
| MIP-1β (CCL4) | 16 | Ok | ✅ | 0.983 | 0.968–1.00 | 0.026 | * | ⬇ | |
| RANTES (CCL5) | 16 | Ok | ✅ | 0.990 | 0.933–1.05 | 0.748 | ⬇ | ||
| SDF-1α | 16 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.494 | ⬇ | ||
| TNF-α | 3 | Sparse Data | ❌ | 0.362 | 0.234–0.56 | 0.000 | *** | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
| Associations between CSF biomarkers and counts of migration events across the BBB | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Negative Binomial Models | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 16 | Ok | ✅ | 1.014 | 0.8655–1.19 | 0.866 | ⬆ | ||
| GM-CSF | 11 | Ok | ✅ | 0.892 | 0.7872–1.01 | 0.071 | . | ⬇ | |
| GRO-alpha (CXCL1) | 16 | Ok | ✅ | 0.988 | 0.9817–1.00 | 0.001 | *** | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 0.330 | 0.0082–13.26 | 0.557 | ⬇ | ||
| IL-1β | 7 | Ok | ✅ | 0.875 | 0.7033–1.09 | 0.234 | ⬇ | ||
| IL-1RA | 15 | Ok | ✅ | 1.000 | 0.9995–1.00 | 0.001 | *** | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.393 | 0.2965–0.52 | 0.000 | *** | ⬇ | |
| IL-5 | 7 | Ok | ✅ | 0.926 | 0.7871–1.09 | 0.354 | ⬇ | ||
| IL-6 | 15 | Ok | ✅ | 0.998 | 0.9972–1.00 | 0.001 | *** | ⬇ | |
| IL-7 | 16 | Ok | ✅ | 1.010 | 0.2351–4.34 | 0.989 | ⬆ | ||
| IL-8 (CXCL8) | 16 | Ok | ✅ | 0.999 | 0.9970–1.00 | 0.174 | ⬇ | ||
| IL-9 | 1 | Sparse Data | ❌ | 1.106 | 0.6638–1.84 | 0.699 | ⬆ | ||
| IL-10 | 10 | Ok | ✅ | 0.952 | 0.7445–1.22 | 0.698 | ⬇ | ||
| IL-15 | 7 | Ok | ✅ | 0.413 | 0.1850–0.92 | 0.031 | * | ⬇ | |
| IL-17A/CTLA-8 | 2 | Sparse Data | ❌ | 0.825 | 0.2375–2.86 | 0.762 | ⬇ | ||
| IL-18 | 16 | Ok | ✅ | 1.010 | 0.9922–1.03 | 0.284 | ⬆ | ||
| IL-31 | 1 | Sparse Data | ❌ | 0.968 | 0.7310–1.28 | 0.820 | ⬇ | ||
| IP-10 (CXCL10) | 16 | Ok | ✅ | 1.000 | 0.9986–1.00 | 0.834 | ⬆ | ||
| MCP-1 (CCL2) | 16 | Ok | ✅ | 1.000 | 0.9989–1.00 | 0.854 | ⬇ | ||
| MIP-1α (CCL3) | 16 | Ok | ✅ | 0.876 | 0.7832–0.98 | 0.020 | * | ⬇ | |
| MIP-1β (CCL4) | 16 | Ok | ✅ | 0.982 | 0.9598–1.01 | 0.126 | ⬇ | ||
| RANTES (CCL5) | 16 | Ok | ✅ | 1.007 | 0.9299–1.09 | 0.872 | ⬆ | ||
| SDF-1α | 16 | Ok | ✅ | 1.000 | 0.9990–1.00 | 0.848 | ⬆ | ||
| TNF-α | 3 | Sparse Data | ❌ | 0.357 | 0.2330–0.55 | 0.000 | *** | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
| Associations between CSF biomarkers and counts of migration events within CNS | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Negative Binomial Models | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 15 | Ok | ✅ | 1.046 | 8.5e-01–1.294 | 0.680 | ⬆ | ||
| GM-CSF | 10 | Ok | ✅ | 0.897 | 7.2e-01–1.114 | 0.326 | ⬇ | ||
| GRO-alpha (CXCL1) | 15 | Ok | ✅ | 0.987 | 9.8e-01–0.998 | 0.019 | * | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 0.001 | 1.5e-05–0.039 | 0.000 | *** | ⬇ | |
| IL-1β | 6 | Ok | ✅ | 0.878 | 6.8e-01–1.128 | 0.309 | ⬇ | ||
| IL-1RA | 14 | Ok | ✅ | 0.999 | 1.0e+00–1.000 | 0.000 | *** | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.405 | 2.4e-01–0.683 | 0.001 | *** | ⬇ | |
| IL-5 | 7 | Ok | ✅ | 1.127 | 7.6e-01–1.678 | 0.558 | ⬆ | ||
| IL-6 | 14 | Ok | ✅ | 0.998 | 1.0e+00–0.999 | 0.004 | ** | ⬇ | |
| IL-7 | 15 | Ok | ✅ | 0.602 | 1.3e-01–2.870 | 0.524 | ⬇ | ||
| IL-8 (CXCL8) | 15 | Ok | ✅ | 0.999 | 1.0e+00–1.002 | 0.586 | ⬇ | ||
| IL-9 | 1 | Sparse Data | ❌ | 0.491 | 2.0e-01–1.225 | 0.127 | ⬇ | ||
| IL-10 | 9 | Ok | ✅ | 1.019 | 7.2e-01–1.434 | 0.915 | ⬆ | ||
| IL-15 | 7 | Ok | ✅ | 0.171 | 4.0e-02–0.735 | 0.018 | * | ⬇ | |
| IL-17A/CTLA-8 | 2 | Sparse Data | ❌ | 1.849 | 2.8e-01–12.424 | 0.527 | ⬆ | ||
| IL-18 | 15 | Ok | ✅ | 1.018 | 9.9e-01–1.047 | 0.212 | ⬆ | ||
| IL-31 | 1 | Sparse Data | ❌ | 0.890 | 6.4e-01–1.242 | 0.493 | ⬇ | ||
| IP-10 (CXCL10) | 15 | Ok | ✅ | 1.000 | 1.0e+00–1.001 | 0.875 | ⬇ | ||
| MCP-1 (CCL2) | 15 | Ok | ✅ | 0.999 | 1.0e+00–1.001 | 0.281 | ⬇ | ||
| MIP-1α (CCL3) | 15 | Ok | ✅ | 0.915 | 8.0e-01–1.048 | 0.200 | ⬇ | ||
| MIP-1β (CCL4) | 15 | Ok | ✅ | 0.972 | 9.4e-01–1.010 | 0.141 | ⬇ | ||
| RANTES (CCL5) | 15 | Ok | ✅ | 0.920 | 8.2e-01–1.035 | 0.165 | ⬇ | ||
| SDF-1α | 15 | Ok | ✅ | 1.000 | 1.0e+00–1.001 | 0.537 | ⬇ | ||
| TNF-α | 3 | Sparse Data | ❌ | 0.462 | 2.0e-01–1.042 | 0.063 | . | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
Viral Suppression: Individuals with detectable viral load >1000 copies/µl are excluded (eligible: LG44 and LG48).
| Associations between CSF biomarkers and counts of migration events from the CNS to the periphery | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Negative Binomial Models (per transition) | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 13 | Ok | ✅ | 1.049 | 0.923–1.19 | 0.463 | ⬆ | ||
| GM-CSF | 8 | Ok | ✅ | 0.925 | 0.855–1.00 | 0.054 | . | ⬇ | |
| GRO-alpha (CXCL1) | 13 | Ok | ✅ | 0.995 | 0.991–1.00 | 0.033 | * | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 0.134 | 0.018–1.02 | 0.052 | . | ⬇ | |
| IL-1β | 5 | Ok | ✅ | 0.973 | 0.846–1.12 | 0.697 | ⬇ | ||
| IL-1RA | 12 | Ok | ✅ | 1.000 | 1.000–1.00 | 0.000 | *** | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.695 | 0.532–0.91 | 0.008 | ** | ⬇ | |
| IL-5 | 5 | Ok | ✅ | 0.973 | 0.885–1.07 | 0.574 | ⬇ | ||
| IL-6 | 12 | Ok | ✅ | 0.999 | 0.999–1.00 | 0.018 | * | ⬇ | |
| IL-7 | 13 | Ok | ✅ | 1.139 | 0.420–3.09 | 0.798 | ⬆ | ||
| IL-8 (CXCL8) | 13 | Ok | ✅ | 0.998 | 0.997–1.00 | 0.000 | *** | ⬇ | |
| IL-9 | 1 | Sparse Data | ❌ | 0.853 | 0.591–1.23 | 0.394 | ⬇ | ||
| IL-10 | 8 | Ok | ✅ | 0.608 | 0.455–0.81 | 0.001 | *** | ⬇ | |
| IL-15 | 5 | Ok | ✅ | 0.827 | 0.488–1.40 | 0.480 | ⬇ | ||
| IL-17A/CTLA-8 | 1 | Sparse Data | ❌ | 0.543 | 0.198–1.49 | 0.236 | ⬇ | ||
| IL-18 | 13 | Ok | ✅ | 1.003 | 0.991–1.02 | 0.597 | ⬆ | ||
| IL-31 | 1 | Sparse Data | ❌ | 1.023 | 0.863–1.21 | 0.795 | ⬆ | ||
| IP-10 (CXCL10) | 13 | Ok | ✅ | 1.000 | 0.998–1.00 | 0.877 | ⬇ | ||
| MCP-1 (CCL2) | 13 | Ok | ✅ | 0.999 | 0.999–1.00 | 0.000 | *** | ⬇ | |
| MIP-1α (CCL3) | 13 | Ok | ✅ | 0.960 | 0.889–1.04 | 0.294 | ⬇ | ||
| MIP-1β (CCL4) | 13 | Ok | ✅ | 0.983 | 0.968–1.00 | 0.020 | * | ⬇ | |
| RANTES (CCL5) | 13 | Ok | ✅ | 1.004 | 0.959–1.05 | 0.874 | ⬆ | ||
| SDF-1α | 13 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.895 | ⬇ | ||
| TNF-α | 2 | Sparse Data | ❌ | 0.598 | 0.448–0.80 | 0.000 | *** | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
| Associations between CSF biomarkers and counts of migration events from the periphery to the CNS | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Negative Binomial Models (per transition) | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 15 | Ok | ✅ | 1.038 | 0.932–1.16 | 0.501 | ⬆ | ||
| GM-CSF | 10 | Ok | ✅ | 0.898 | 0.832–0.97 | 0.005 | ** | ⬇ | |
| GRO-alpha (CXCL1) | 15 | Ok | ✅ | 0.993 | 0.989–1.00 | 0.005 | ** | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 1.373 | 0.098–19.24 | 0.814 | ⬆ | ||
| IL-1β | 7 | Ok | ✅ | 0.889 | 0.765–1.03 | 0.127 | ⬇ | ||
| IL-1RA | 14 | Ok | ✅ | 1.000 | 1.000–1.00 | 0.015 | * | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.550 | 0.437–0.69 | 0.000 | *** | ⬇ | |
| IL-5 | 6 | Ok | ✅ | 0.946 | 0.845–1.06 | 0.337 | ⬇ | ||
| IL-6 | 14 | Ok | ✅ | 0.999 | 0.998–1.00 | 0.013 | * | ⬇ | |
| IL-7 | 15 | Ok | ✅ | 0.684 | 0.217–2.16 | 0.518 | ⬇ | ||
| IL-8 (CXCL8) | 15 | Ok | ✅ | 0.999 | 0.998–1.00 | 0.313 | ⬇ | ||
| IL-9 | 1 | Sparse Data | ❌ | 1.297 | 0.922–1.82 | 0.135 | ⬆ | ||
| IL-10 | 10 | Ok | ✅ | 0.848 | 0.729–0.99 | 0.032 | * | ⬇ | |
| IL-15 | 6 | Ok | ✅ | 0.543 | 0.325–0.91 | 0.020 | * | ⬇ | |
| IL-17A/CTLA-8 | 2 | Sparse Data | ❌ | 0.701 | 0.295–1.66 | 0.420 | ⬇ | ||
| IL-18 | 15 | Ok | ✅ | 0.999 | 0.986–1.01 | 0.818 | ⬇ | ||
| IL-31 | 1 | Sparse Data | ❌ | 0.955 | 0.787–1.16 | 0.643 | ⬇ | ||
| IP-10 (CXCL10) | 15 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.933 | ⬆ | ||
| MCP-1 (CCL2) | 15 | Ok | ✅ | 1.000 | 0.999–1.00 | 0.851 | ⬇ | ||
| MIP-1α (CCL3) | 15 | Ok | ✅ | 0.928 | 0.852–1.01 | 0.082 | . | ⬇ | |
| MIP-1β (CCL4) | 15 | Ok | ✅ | 0.991 | 0.974–1.01 | 0.277 | ⬇ | ||
| RANTES (CCL5) | 15 | Ok | ✅ | 1.017 | 0.965–1.07 | 0.522 | ⬆ | ||
| SDF-1α | 15 | Ok | ✅ | 1.000 | 1.000–1.00 | 0.862 | ⬆ | ||
| TNF-α | 3 | Sparse Data | ❌ | 0.488 | 0.357–0.67 | 0.000 | *** | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
| Associations between CSF biomarkers and counts of migration events across the BBB | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Negative Binomial Models (per transition) | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 15 | Ok | ✅ | 1.038 | 0.94–1.15 | 0.460 | ⬆ | ||
| GM-CSF | 10 | Ok | ✅ | 0.909 | 0.85–0.98 | 0.008 | ** | ⬇ | |
| GRO-alpha (CXCL1) | 15 | Ok | ✅ | 0.994 | 0.99–1.00 | 0.006 | ** | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 0.881 | 0.08–9.65 | 0.917 | ⬇ | ||
| IL-1β | 7 | Ok | ✅ | 0.901 | 0.79–1.03 | 0.137 | ⬇ | ||
| IL-1RA | 14 | Ok | ✅ | 1.000 | 1.00–1.00 | 0.005 | ** | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.586 | 0.47–0.73 | 0.000 | *** | ⬇ | |
| IL-5 | 6 | Ok | ✅ | 0.955 | 0.86–1.06 | 0.388 | ⬇ | ||
| IL-6 | 14 | Ok | ✅ | 0.999 | 1.00–1.00 | 0.012 | * | ⬇ | |
| IL-7 | 15 | Ok | ✅ | 0.754 | 0.26–2.15 | 0.598 | ⬇ | ||
| IL-8 (CXCL8) | 15 | Ok | ✅ | 0.999 | 1.00–1.00 | 0.231 | ⬇ | ||
| IL-9 | 1 | Sparse Data | ❌ | 1.215 | 0.88–1.67 | 0.230 | ⬆ | ||
| IL-10 | 10 | Ok | ✅ | 0.860 | 0.75–0.99 | 0.033 | * | ⬇ | |
| IL-15 | 6 | Ok | ✅ | 0.603 | 0.37–0.98 | 0.042 | * | ⬇ | |
| IL-17A/CTLA-8 | 2 | Sparse Data | ❌ | 0.706 | 0.32–1.56 | 0.388 | ⬇ | ||
| IL-18 | 15 | Ok | ✅ | 0.999 | 0.99–1.01 | 0.828 | ⬇ | ||
| IL-31 | 1 | Sparse Data | ❌ | 0.960 | 0.81–1.14 | 0.649 | ⬇ | ||
| IP-10 (CXCL10) | 15 | Ok | ✅ | 1.000 | 1.00–1.00 | 0.902 | ⬆ | ||
| MCP-1 (CCL2) | 15 | Ok | ✅ | 1.000 | 1.00–1.00 | 0.649 | ⬇ | ||
| MIP-1α (CCL3) | 15 | Ok | ✅ | 0.934 | 0.86–1.01 | 0.081 | . | ⬇ | |
| MIP-1β (CCL4) | 15 | Ok | ✅ | 0.990 | 0.98–1.01 | 0.211 | ⬇ | ||
| RANTES (CCL5) | 15 | Ok | ✅ | 1.016 | 0.97–1.07 | 0.511 | ⬆ | ||
| SDF-1α | 15 | Ok | ✅ | 1.000 | 1.00–1.00 | 0.921 | ⬆ | ||
| TNF-α | 3 | Sparse Data | ❌ | 0.525 | 0.40–0.70 | 0.000 | *** | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
| Associations between CSF biomarkers and counts of migration events within CNS | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Negative Binomial Models (per transition) | |||||||||
| Marker | Samples Detectable1 | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| Eotaxin (CCL11) | 15 | Ok | ✅ | 1.304 | 0.91–1.88 | 0.154 | ⬆ | ||
| GM-CSF | 10 | Ok | ✅ | 0.711 | 0.35–1.43 | 0.338 | ⬇ | ||
| GRO-alpha (CXCL1) | 15 | Ok | ✅ | 0.519 | 0.28–0.96 | 0.038 | * | ⬇ | |
| IL-1α | 1 | Sparse Data | ❌ | 0.684 | 0.52–0.90 | 0.006 | ** | ⬇ | |
| IL-1β | 6 | Ok | ✅ | 0.966 | 0.53–1.76 | 0.910 | ⬇ | ||
| IL-1RA | 14 | Ok | ✅ | 0.476 | 0.33–0.69 | 0.000 | *** | ⬇ | |
| IL-2 | 2 | Sparse Data | ❌ | 0.576 | 0.37–0.90 | 0.015 | * | ⬇ | |
| IL-5 | 7 | Ok | ✅ | 0.835 | 0.40–1.75 | 0.633 | ⬇ | ||
| IL-6 | 14 | Ok | ✅ | 0.510 | 0.28–0.92 | 0.024 | * | ⬇ | |
| IL-7 | 15 | Ok | ✅ | 1.083 | 0.67–1.75 | 0.746 | ⬆ | ||
| IL-8 (CXCL8) | 15 | Ok | ✅ | 0.796 | 0.54–1.17 | 0.247 | ⬇ | ||
| IL-9 | 1 | Sparse Data | ❌ | 0.653 | 0.48–0.89 | 0.007 | ** | ⬇ | |
| IL-10 | 9 | Ok | ✅ | 1.061 | 0.68–1.66 | 0.797 | ⬆ | ||
| IL-15 | 7 | Ok | ✅ | 0.666 | 0.44–1.02 | 0.060 | . | ⬇ | |
| IL-17A/CTLA-8 | 2 | Sparse Data | ❌ | 1.232 | 0.85–1.78 | 0.266 | ⬆ | ||
| IL-18 | 15 | Ok | ✅ | 1.492 | 0.83–2.68 | 0.181 | ⬆ | ||
| IL-31 | 1 | Sparse Data | ❌ | 1.146 | 0.64–2.04 | 0.644 | ⬆ | ||
| IP-10 (CXCL10) | 15 | Ok | ✅ | 1.195 | 0.86–1.66 | 0.292 | ⬆ | ||
| MCP-1 (CCL2) | 15 | Ok | ✅ | 0.495 | 0.30–0.82 | 0.006 | ** | ⬇ | |
| MIP-1α (CCL3) | 15 | Ok | ✅ | 0.834 | 0.46–1.51 | 0.551 | ⬇ | ||
| MIP-1β (CCL4) | 15 | Ok | ✅ | 0.881 | 0.60–1.30 | 0.524 | ⬇ | ||
| RANTES (CCL5) | 15 | Ok | ✅ | 1.015 | 0.68–1.51 | 0.941 | ⬆ | ||
| SDF-1α | 15 | Ok | ✅ | 1.079 | 0.66–1.75 | 0.758 | ⬆ | ||
| TNF-α | 3 | Sparse Data | ❌ | 0.555 | 0.32–0.95 | 0.032 | * | ⬇ | |
| 1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates. | |||||||||
Viral Suppression: Individuals with detectable viral load >1000 copies/µl are excluded (eligible: LG30, LG44, LG48 and LG51).
| Associations between Reservoirs Measures and counts of migration events from the CNS to the periphery | ||||||||
|---|---|---|---|---|---|---|---|---|
| Negative Binomial Models (per transition) | ||||||||
| Reservoir Measures | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| HIV DNA (source) | Ok | ✅ | 0.885 | 7.1e-01–1.1e+00 | 0.277 | ⬇ | ||
| HIV DNA (destination) | Ok | ✅ | 1.017 | 8.6e-01–1.2e+00 | 0.847 | ⬆ | ||
| Viral Diversity (source) | Ok | ✅ | >1e6 | 5.4e+04–+Inf | 0.006 | ** | ⬆ | |
| Viral Diversity (destination) | Ok | ✅ | >1e6 | 7.0e-03–+Inf | 0.143 | ⬆ | ||
| Viral Divergence | Wide CI | ❌ | 4.141 | <1e-6–+Inf | 0.904 | ⬆ | ||
| Associations between Reservoirs Measures and counts of migration events from the periphery to the CNS | ||||||||
|---|---|---|---|---|---|---|---|---|
| Negative Binomial Models (per transition) | ||||||||
| Reservoir Measures | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| HIV DNA (source) | Ok | ✅ | 1.085 | 9.8e-01–1.2e+00 | 0.114 | ⬆ | ||
| HIV DNA (destination) | Ok | ✅ | 1.052 | 9.6e-01–1.2e+00 | 0.287 | ⬆ | ||
| Viral Diversity (source) | Ok | ✅ | 82.170 | 1.8e-04–+Inf | 0.507 | ⬆ | ||
| Viral Diversity (destination) | Ok | ✅ | 4.366 | 3.7e-06–+Inf | 0.836 | ⬆ | ||
| Viral Divergence | Wide CI | ❌ | 0.159 | <1e-6–+Inf | 0.842 | ⬇ | ||
| Associations between Reservoirs Measures and counts of migration events across the BBB | ||||||||
|---|---|---|---|---|---|---|---|---|
| Negative Binomial Models (per transition) | ||||||||
| Reservoir Measures | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| HIV DNA (source) | Ok | ✅ | 1.079 | 1.0e+00–1.2e+00 | 0.064 | . | ⬆ | |
| HIV DNA (destination) | Ok | ✅ | 0.995 | 9.3e-01–1.1e+00 | 0.885 | ⬇ | ||
| Viral Diversity (source) | Ok | ✅ | 950493.238 | 1.0e+01–+Inf | 0.019 | * | ⬆ | |
| Viral Diversity (destination) | Ok | ✅ | 19.716 | 2.3e-04–+Inf | 0.607 | ⬆ | ||
| Viral Divergence | Wide CI | ❌ | 0.254 | <1e-6–+Inf | 0.863 | ⬇ | ||
| Associations between Reservoirs Measures and counts of migration events within CNS | ||||||||
|---|---|---|---|---|---|---|---|---|
| Negative Binomial Models (per transition) | ||||||||
| Reservoir Measures | Effect | Status | exp(Beta) | 95% CI | p-value | Significance | Direction | |
| HIV DNA (source) | Ok | ✅ | 0.739 | 6.3e-01–8.6e-01 | 0.000 | *** | ⬇ | |
| HIV DNA (destination) | Ok | ✅ | 1.059 | 9.4e-01–1.2e+00 | 0.365 | ⬆ | ||
| Viral Diversity (source) | Wide CI | ❌ | 296.502 | <1e-6–+Inf | 0.571 | ⬆ | ||
| Viral Diversity (destination) | Ok | ✅ | 14999.046 | 9.3e-04–+Inf | 0.256 | ⬆ | ||
| Viral Divergence | Wide CI | ❌ | 5.242 | <1e-6–+Inf | 0.868 | ⬆ | ||
Note
| 🧠 Last Gift cohort HIV Reservoir CNS Clusters | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cluster1 | Cluster size | tMRCA (median) | lower HPD | upper HPD | Basal Ganglia | Frontal Cortex (motor) | Hippocampus | Occipital Cortex | Spinal Cord (cervical) | Spinal Cord (lumbosacral) | Spinal Cord (thoracic) | Pons | Medulla | Parietal Cortex |
| LG03 | ||||||||||||||
| Cluster 1 | 17 | 2005-02-01 | 1994-04-01 | 2011-05-22 | 4 (24%) | 1 (6%) | 2 (12%) | 1 (6%) | 3 (18%) | 3 (18%) | 3 (18%) | – | – | – |
| LG05 | ||||||||||||||
| Cluster 1 | 17 | 2015-09-11 | 2013-05-12 | 2017-03-06 | 6 (35%) | 5 (29%) | 1 (6%) | 1 (6%) | – | – | – | 4 (24%) | – | – |
| LG14 | ||||||||||||||
| Cluster 1 | 21 | 2014-06-10 | 2011-12-16 | 2016-05-11 | 7 (33%) | – | – | – | 5 (24%) | 4 (19%) | – | 5 (24%) | – | – |
| LG18 | ||||||||||||||
| Cluster 1 | 6 | 2005-12-08 | 1994-11-26 | 2014-03-02 | – | – | – | – | – | – | – | – | 6 (100%) | – |
| LG22 | ||||||||||||||
| Cluster 1 | 10 | 2022-05-26 | 2022-01-14 | 2022-08-28 | – | – | – | – | – | – | 8 (80%) | – | 2 (20%) | – |
| LG23 | ||||||||||||||
| Cluster 1 | 6 | 2020-10-29 | 2020-06-15 | 2021-02-12 | – | – | – | 1 (17%) | 1 (17%) | 4 (67%) | – | – | – | – |
| LG25 | ||||||||||||||
| Cluster 1 | 8 | 2020-11-27 | 2020-08-15 | 2021-02-09 | 4 (50%) | 2 (25%) | – | – | – | – | 2 (25%) | – | – | – |
| LG29 | ||||||||||||||
| Cluster 1 | 6 | 2021-01-27 | 2019-07-31 | 2021-07-10 | 6 (100%) | – | – | – | – | – | – | – | – | – |
| LG30 | ||||||||||||||
| Cluster 1 | 20 | 2020-03-27 | 2018-02-20 | 2021-07-13 | 8 (40%) | – | 1 (5%) | – | – | – | 1 (5%) | 8 (40%) | 2 (10%) | – |
| Cluster 2 | 7 | 2018-05-23 | 2012-03-28 | 2021-03-05 | – | – | 5 (71%) | 2 (29%) | – | – | – | – | – | – |
| Cluster 3 | 7 | 2021-10-31 | 2020-04-27 | 2022-09-08 | – | – | – | – | – | – | – | – | – | 7 (100%) |
| LG33 | ||||||||||||||
| Cluster 1 | 38 | 2008-09-26 | 2003-03-23 | 2012-11-15 | 5 (13%) | 8 (21%) | 1 (3%) | 7 (18%) | – | 5 (13%) | – | 3 (8%) | – | 9 (24%) |
| Cluster 2 | 15 | 2008-06-27 | 2002-06-12 | 2012-10-19 | – | – | – | – | 4 (27%) | – | 8 (53%) | – | 3 (20%) | – |
| LG42 | ||||||||||||||
| Cluster 1 | 22 | 2023-05-14 | 2023-02-03 | 2023-07-29 | – | – | – | – | 3 (14%) | 5 (23%) | 6 (27%) | 7 (32%) | 1 (5%) | – |
| LG44 | ||||||||||||||
| Cluster 1 | 12 | 2021-06-27 | 2019-11-11 | 2023-03-15 | – | – | – | – | 1 (8%) | – | 4 (33%) | 3 (25%) | 4 (33%) | – |
| Cluster 2 | 17 | 2020-01-31 | 2017-11-24 | 2021-06-29 | – | – | – | – | 6 (35%) | 5 (29%) | 2 (12%) | 2 (12%) | 2 (12%) | – |
| LG48 | ||||||||||||||
| Cluster 1 | 6 | 2024-02-18 | 2023-02-24 | 2024-09-29 | – | – | – | – | 1 (17%) | 2 (33%) | 1 (17%) | 1 (17%) | 1 (17%) | – |
| Cluster 2 | 7 | 2023-07-27 | 2021-07-20 | 2024-08-02 | – | – | – | – | – | – | – | 3 (43%) | 4 (57%) | – |
| LG49 | ||||||||||||||
| Cluster 1 | 8 | 2021-07-09 | 2017-06-11 | 2023-08-19 | – | – | – | – | 4 (50%) | – | 2 (25%) | 2 (25%) | – | – |
| 1 Using most recently single genome sequencing env data | ||||||||||||||
HIV Persistence & Dynamics | Research Presentation